Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data

Brewster, L. R.; Dale, J. J.; Guttridge, T. L.; Gruber, S. H.; Hansell, A. C.; Elliott, M.; Cowx, I. G.; Whitney, N. M.; Gleiss, A. C.

doi:10.1007/s00227-018-3318-y

All Outputs (2)

Oil and gas infrastructure decommissioning in marine protected areas: System complexity, analysis and challenges (2018)
Journal Article
Burdon, D., Barnard, S., Boyes, S. J., & Elliott, M. (2018). Oil and gas infrastructure decommissioning in marine protected areas: System complexity, analysis and challenges. Marine pollution bulletin, 135, 739-758. https://doi.org/10.1016/j.marpolbul.2018.07.077

Many offshore oil and gas production facilities are nearing the end of their operational life, with decommissioning now becoming a global challenge. The compatibility of decommissioning operations to marine protected areas (MPAs) creates further chal... Read More about Oil and gas infrastructure decommissioning in marine protected areas: System complexity, analysis and challenges.

Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data (2018)
Journal Article
Brewster, L. R., Dale, J. J., Guttridge, T. L., Gruber, S. H., Hansell, A. C., Elliott, M., …Gleiss, A. C. (2018). Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data. Marine Biology, 165(4), Article 62. https://doi.org/10.1007/s00227-018-3318-y

© 2018, The Author(s). Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals... Read More about Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data.